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Sum( data[i] ) / data.length
.
auto(data, 1, mean, variance)
.
z = slope*x + intercept
by minimizing the sum of least squares
between z
and the y
.
x
under the beta density
function.
x
to
infinity) of the beta density function.
0
through k
of the Binomial
probability density.
k+1
through n
of the Binomial
probability density.
chi2
, or the
"omnibus" test statistic, and the significance, or "p-value" of this test statistic.
x
)
of the Chi square probability density function with
v
degrees of freedom.
x
to
infinity) of the Chi square probability density function
with v
degrees of freedom.
a
and b
into a new array
c
such that c = {a,b}
.
size
with
each element equal to the specified constValue
.
size
with
each element equal to the specified constValue
.
y
for a sequence of λ
and returns the array of linear correlation coefficients
between the x
and the box-cox transformed y
.
data1
and data2
,
each of length N.
value
.
ScatterPlot.drawScatterPlot(float[], float[], float, int)
.
Correlation.auto(float[], int, float, float)
)
in the residuals (prediction errors) from a regression analysis.
erfc
.
x
under the F density
function (also known as Snedcor's density or the
variance ratio density); formerly named fdtr
.
fdtrc
.
x
of the F density is equal to p; formerly named fdtri
.
Γ(x)
x
of the gamma probability
density function.
x
to infinity of the gamma
probability density function:
L
.
data[i]
is NaN.
data[i]
is NaN.
U
Sum( 1.0 / data[i])
.
dimension
xx
; formerly named ibeta
.
igamma
.
Cast
function.
fromIndex
(inclusive) to toIndex
(exclusive)
in ascending or descending order.
Cast
function.
replaceZeroWith
value instead.
Sum( 1.0 / data[i])
.
-3 + moment(data,4,mean) / standardDeviation4
.
zb2
associated with
b2 and the significance, or "p-value" of the kurtosis test statistic zb2
, assuming a
two-tailed null hypothesis as well as .
r
between two datasets under the null hypothesis of no correlation.
x
and y
into
a linear one.start
(inclusive) to end
(inclusive):
y[0] = start; y[1] = start+1; ...
- linspace(int, int, int) -
Static method in class papaya.Mat
-
- linspace(float, float, float) -
Static method in class papaya.Mat
-
- log(float[]) -
Static method in class papaya.Mat
- Returns an array with each element equal to the natural logarithm (the base-e logarithm)
the corresponding input array.
- log10(float[]) -
Static method in class papaya.Mat
- Returns an array with each element equal to the log 10 value (base 10 logarithm)
of the corresponding input array.
- logGamma(double) -
Static method in class papaya.Gamma
- Returns the natural logarithm of the gamma function; formerly named
lgamma
.
- LOGPI -
Static variable in interface papaya.PapayaConstants
-
- logs(float[], int, int) -
Static method in class papaya.Descriptive.Sum
- Returns the sum of logarithms of a data sequence, which is
Sum( Log(data[i])
.
- logToBase(float[], float) -
Static method in class papaya.Mat
- Returns an array with each element equal to the log A value
of the corresponding input array.
- LU - Class in papaya
- LU Decomposition.
- LU(float[][]) -
Constructor for class papaya.LU
- Constructor.
U
and p-value for
assessing whether one of two samples of independent observations tends to have
larger values than the other.
Sum( data[i] ) / data.length
.
k
data sequences.
k
-th order with constant c
of a data sequence,
which is Sum( (data[i]-c)k ) / data.size()
.
0
through k
of the Negative Binomial Distribution.
k+1
to infinity of the Negative
Binomial distribution.
N
) necessary to produce a Q-Q plot
for a normal distribution (or normal probability plot).
x
(assumes mean is zero, variance is one).
x
when the
data has not been standardized (i.e.
x
, for which the area under the
Normal (Gaussian) probability density function (integrated from
minus infinity to x
) is equal to the argument y
(assumes mean is zero, variance is one); formerly named ndtri
.
F = variance between samples / variance within samples
.lowerLimit
and more than or equal to the upperLimit
k
terms of the Poisson distribution.
k+1
to Infinity
of the Poisson distribution.
Sum( (data[i]-c)k )
;
optimized for common parameters like c == 0.0
and/or k == -2 ..
- powerDeviations(float[], int, float, int, int) -
Static method in class papaya.Descriptive.Sum
- Returns
Sum( (data[i]-c)k )
for all i = from ..
- powers(float[], int) -
Static method in class papaya.Descriptive.Sum
- Returns the sum of powers of a data sequence, which is
Sum ( data[i]k )
.
- print(double[], int) -
Static method in class papaya.Mat
- Print the array to the screen in a single line.
- print(double[][], int) -
Static method in class papaya.Mat
- Print the matrix to the screen with each row of the
matrix taking up one line.
- print(double[][], String[], String[], int) -
Static method in class papaya.Mat
- Print the matrix to the screen with the columns and rows labeled according to the
input strings.
- print(float[], int) -
Static method in class papaya.Mat
- Print the array to the screen in a single line.
- print(float[][], int) -
Static method in class papaya.Mat
- Print the matrix to the screen with each row of the
matrix taking up one line.
- print(float[][], String[], String[], int) -
Static method in class papaya.Mat
- Print the matrix to the screen with the columns and rows labeled according to the
input strings.
- print(int[], int) -
Static method in class papaya.Mat
- Print the array to the screen in a single line.
- print(int[][], int) -
Static method in class papaya.Mat
- Print the matrix to the screen with each row of the
matrix taking up one line.
- print(int[][], String[], String[], int) -
Static method in class papaya.Mat
- Print the matrix to the screen with the columns and rows labeled according to the
input strings.
- Probability - Class in papaya
- Cumulative distribution functions and corresponding inverses of certain probability distributions.
- product(int, float) -
Static method in class papaya.Descriptive
- Returns the product, which is
Prod( data[i] )
.
- product(float[]) -
Static method in class papaya.Descriptive
- Returns the product of a data sequence, which is
Prod( data[i] )
.
- products(float[], float[]) -
Static method in class papaya.Descriptive.Sum
- Returns the sum of the product of two data arrays,
Sum( x[i] * y[i])
.
- pValue() -
Method in class papaya.OneWayAnova
- Returns the significance, or "p-value" of the test statistic
OneWayAnova.F()
.
- pValues(float[]) -
Static method in class papaya.Normality.Dago
- Returns an array containing the three significance, or p-values, for testing normality.
phi-
quantile; that is, an element elem
for which holds that phi
percent of data elements are less than
elem
.
<= element
.
nanStrategy
and ties
resolved using tiesStrategy
.
nanStrategy
and ties
resolved using tiesStrategy
.
tiesStrategy
.
tiesStrategy
.
oldValue
with
the newValue
.
oldValue
are replaced with
the newValue
.
Delta_i = z_i - y_i
,
where
z_i = (slope*x_i + intercept)
is the best fit linear line.
Delta_i = z_i - y_i
,
where
z_i = (slope*x_i + intercept)
is the best fit linear line.
ScatterPlot.setDataExtremes(float, float, float, float)
moment(data,3,mean) / standardDeviation3
.
zb1
associated with sqrt(b1) and
the significance, or "p-value" of the skew test statistic zb1
, assuming a
two-tailed null hypothesis as well as .
rho
between two datasets under the null hypothesis of no correlation.
rho
, between multiple columns of a matrix
with each column corresponding to a dataset, and each row an observation.
rho
.
t
of the Student-t
distribution with k > 0
degrees of freedom.
t
, for which the area under the
Student-t probability density function (integrated from
minus infinity to t
) is equal to p.
x
and y
arrays according to
x
and going from fromIndex
(inclusive) to toIndex
(exclusive)
in ascending or descending order.
k
data sequences.
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